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崎岖边缘生存事件

Trend | Insight |解决方案

随需应变的现在

什么是DPU(数据处理单元)?


什么是DPU(数据处理单元)?

数据处理单元dpu是一种新型的重新编程高-表现处理器结合高-性能的网络接口优化执行和加速网络和存储数据中心服务器执行的功能。DPUs插入服务器的PCIe插槽就像GPU那样他们允许服务器卸载网络和存储功能从CPU到DPU,允许CPU焦点只有在运行操作系统和系统的应用。DPUs经常使用重新编程的FPGA与网络接口卡相结合,以加速网络流量就像gpu一样正在用于加速人工智能(AI)应用通过卸载数学从CPU到GPU的所有操作。过去,使用GPU提供丰富的、实时的图形这是因为他们可以并行处理大量数据蛇行他们非常适合加速人工智能工作负载,如机器学习和深度学习,以及其他人工智能工作。



DPU加速服务器将来会因为他们的卸载能力而变得非常受欢迎吗
从CPU到DPU的网络功能,释放提高宝贵的CPU处理能力,允许CPU运行更多的应用程序并尽可能有效地运行操作系统,而不会通过处理网络活动来逐步丢失。事实上,一些专家声称,30%的CPU处理能力达到处理网络和存储功能。卸载存储和DPU提供网络功能,释放宝贵的CPU处理能力等功能作为虚拟或容器化的工作负载此外,DPU可用于处理包含网络安全性的功能,防火墙任务、加密和基础设施管理。

DPUs将成为数据中心服务器的第三个组件,连同CPU(中央处理单元)和gpu(图形处理单元)因为它们能够加速和执行网络和存储功能。CPU将被使用一般的-目的计算.GPU将被用于加速人工智能应用。T他在一个DPUDPU配置服务器将使用处理数据并在数据中心周围移动数据。

整体随着数据中心存储的数据量的不断增加,dpu的发展前景更加光明S,需要一种可以加速由高性能数据中心服务器执行的存储和网络功能的解决方案。dpu可以为现有的服务器注入新的活力年代,因为他们可以减少服务器的CPU利用率卸载网络和存储功能。估计表明30%的CPU利用率朝向网络功能年代,使它们移动到DPU将为您提供额外的CPU处理能力。因此,DPU可以延长服务器的使用寿命几个月甚至几年取决于用于网络功能的系统资源的多少。

DPU的组成部件是什么?

一个DPU一个系统一个由三种主要元素组成的芯片年代。首先,数据处理单元通常具有软件程序的多核CPU勒。第二个元素是高-性能网络接口,使DPU能够解析,过程和有效的行动数据通过网络.第三个要素是一组丰富的灵活的、可编程的加速引擎,将网络和存储功能从CPU卸载到DPU。DPU通常与智能NIC集成S提供强大的网络数据处理ng

英伟达在这方面处于领先地位在DPU方面,最近发布的Nvidia Bluefield 2 DPU,这是世界上第一个基于芯片架构的数据基础设施,为现代数据中心优化。Bluefield 2 DPU允许数据中心服务器将网络和存储功能从CPU转移到DPU,允许DPU处理普通的存储和网络功能。

英伟达DPU可访问通过DOCA SDK,为DPU硬件启用可编程API。DOCA启用组织年代对dpu进行编程以加速数据处理用于将数据移入和移出服务器、虚拟机和容器。DPUs accelerat处理与虚拟机关联的东西向流量容器南北流量流入和退出数据中心。也就是说,dpu的亮点在于移动数据数据中心中的AR,因为它们针对数据移动进行了优化。

此外,Nvidia声明DPUS能够卸载和加速所有数据中心安全服务es。这是因为它们包含next-一代防火墙、微营销,数据加密功能、入侵检测。在过去安全性由使用x86 CPU的软件处理但是,安全可以卸载到DPU年代,释放CPU资源用于其他任务。

dpu最常见的特性是什么?

dpu有很多特性,但以下是dpu上最常见的特性:

  • -速度连接通过一个或多个100千兆到200千兆接口
  • -包装速度处理
  • 多核处理通过ARM或MIPS的cpu(8倍64位臂CPU核心)
  • 内存控制器为DDR4和DDR5 RAM提供支持
  • 加速器
  • PCI Express Gen 4支持
  • 安全功能
  • 定制的操作系统单独的d从主机系统操作系统

哪些是最常见的DPU解决方案?18luck最新

英伟达发布了一个DPU被称为Nvidia Mellanox BlueField 2 DPU和蓝色的F通过2 x DPUBlueField 2X DPU拥有BlueField 2 DPU的所有功能加上一个额外的安培GPU,使DPU上的人工智能功能.Nvidia在其DPU上安装了一个GPU来处理安全、网络和存储管理例如,机器学习或深度学习可以运行在数据处理单元本身和用于识别和阻止网络入侵未遂。此外,英伟达还表示,计划在2022年推出Bluefield 3,在2023年推出Bluefield 4。

C英特尔(Intel)和Xilinx等公司就是这样引入了一些dpu。也就是说,Xilinx和英特尔的一些产品被称为SmartNICsSmartnics来自Xilinx和英特尔利用fpga来加速网络和存储功能。智能NIC与数据处理单元相同的方式工作,它们卸载网络功能从CPU到SmartNIC,释放处理能力由intelligently将网络和存储功能委托给SmartNIC由于fpga的可重编程特性,fpga给数据路径带来了并行性和定制性。

例如,Xilinx提供了雅尔索系列SmartNICs各种各样的18luck在线娱乐网产品,英特尔它的合作伙伴提供几个fpgasmartnic18luck最新加速大规模数据处理工作负载的解决方案数据中心。英特尔声称SmartNICs“通过将交换、存储和安全功能卸载到集成了英特尔fpga和英特尔至强处理器的单个PCIe平台上,提高数据中心性能水平。”英特尔提供了第二种更新的智能网卡解决方案称为Silicom FPGAsmartnicn5010,它结合了一个英特尔Stratix 10 FPGA与英特尔以太网800系列Ad阿普特为组织提供4x 100千兆以太网端口,为数据中心提供足够的带宽。

为什么dpu越来越受欢迎?

我们生活在一个海量数据的数字信息时代每天生成.随着物联网设备、自动驾驶汽车、互联家庭和互联网络的增多,这一点尤其正确工作场所联机饱和d数据中心有数据。因此,需要解决可以使数据中心能够应对越来越多的数18luck最新据中心的数据中心数据通过数据中心移动。

dpu包含数据移动系统,加速数据移动和处理操作,卸载网络功能一个服务器的处理器到DPU。dpu是一种从服务器中获取更多处理能力的好方法,特别是考虑到摩尔定律已经变慢,迫使组织使用硬件加速器来获得更多的硬件性能,降低组织的总拥有成本,因为可以从现有的硬件中提取更多的性能,允许服务器执行多个应用程序工作负载。

数据处理单元和FPGA Smartnics正在获得普及与微软和谷歌探索他们到他们的数据加速数据处理和人工智能工作负载的中心。此外,英伟达还与VMware展开了合作将组网、安全、存储等任务卸载给DPU处理。

有哪些其他性能加速器?

我们现在将讨论常用于数据中心的其他绩效加速器。我们将讨论的性能加速器包括GPU(图形处理单元),计算存储和FPGA(字段-可编程门阵列)。

1.图形处理单元(gpu)

图形处理单元通常部署在高-数据中心的性能服务器,以加速工作负载。服务器通常会卸载对GPU进行复杂的数学计算因为GPU可以更快地执行它们。这是因为gpu采用了并行架构,这是由m很多小核心,而不是cpus,使他们能够处理并行执行许多任务,这允许组织从服务器中提取更多性能。

源信贷(英伟达)

例如,平均CPU在任何地方都有4到10个核,而图形处理器有数百个或数千一起工作的小核复杂的计算并行因此,gpu不同于cpu,后者有较少更适合顺序数据处理。GPU加速服务器很棒对于高-分辨率视频编辑,医学成像,人工情报,机器学习培训和深度学习培训。

安装在数据中心服务器上的gpu对于加速是很好的害死深度学习培训和机器学习培训,需要大量的计算能力,即CPU根本不提供。GPU比CPU更快地执行人工智能任务,因为他们配备了HBM(高带宽内存和成百上千个可以执行浮动的核心-观点算法速度明显快于传统cpu。

出于这些原因,组织使用gpu来训练深度学习和机器学习模型。数据集越大和神经网络越大,组织就更有可能需要GPU来加速工作负载。虽然cpu可以执行深度学习训练和机器学习训练,都需要很长时间才能进行复杂的计算。有些情况下,深度学习训练需要几个小时但是,只使用一个CPU执行相同的任务可能会几天到几个星期而不是几个小时。

此外,为数据中心服务器增加图形处理器显著更好的数据吞吐量并提供能够处理和分析数据尽可能小的延迟。延迟是指完成给定任务所需的时间,数据吞吐量是指完成的任务数量D每单位时间。

2.计算存储设备(CSD)

计算艾尔存储已经进入了数据中心年代作为性能加速器。计算艾尔存储在存储设备级别处理数据,减少了数据在CPU之间的移动还有存储设备。计算艾尔存储使实时数据分析和改进成为可能系统的表现通过减少输入/输出瓶颈。计算存储设备与常规存储设备相同,但它们包括多核处理器这是用于执行诸如索引数据的功能,因为它进入存储设备并搜索特定条目的存储设备。

源信贷(AnandTech)

计算存储设备越来越受欢迎,由于不断增长的需要,处理和实时分析数据实时数据处理和分析是可能的,因为数据不再在存储设备和CPU之间移动.我nstead,数据在存储设备本身上处理。在数据所在的确切位置为存储介质带来计算能力,可以实现实时分析和决策。

3.现场可编程门阵列

源信贷(Xilinx)

FPGA是集成电路是由逻辑块,I / O单元格和其他允许用户重新编程和重新配置的资源根据不同的芯片方式具体要求啊F您希望它执行的工作负载。FPGAs越来越受欢迎用于执行深度学习推理处理和机器学习推理。此外,FPGA-基于SmartNICs正在使用因为他们能够卸载网络和存储功能从CPU到smartnic网络和存储功能会给系统的CPU带来很大的负担,因此将这些功能转移到SmartNIC上释放宝贵的CPU处理能力运行操作系统和其他关键应用程序。基于FPGA的SmartNICs允许组织优化smartnic对于特定的工作负载这是将被卸载到SmartNIC,提供难以找到的可定制性其他地方。

底线

在这一点上,不应该感到惊讶dpu(数据处理单元)是获得高知名度-性能数据中心服务器由于他们的能力卸载存储和网络功能DPU,让处理器专注于运行操作系统和收入产生应用程序。Premio提供了一些人DPU服务器利用dpu使服务器更强大通过卸载数据pr从CPU到DPU的处理、网络功能和存储功能。nvidia声称单一BlueField2数据处理单元可以处理相同的数据中心服务,将需要125个CPU核心年代,允许DPU服务器聪明地工作,而不是更辛苦。所以,如果有兴趣购买DPU服务器,请随时联系你的DPU服务器专业人士。THey将非常乐意帮助您选择或定制一个解决方案,满足您的特定需求。